Multiresolution analysis

About: Multiresolution analysis is a research topic. Over the lifetime, 4032 publications have been published within this topic receiving 140743 citations. The topic is also known as: Multiresolution analysis, MRA.

MASSIT - Multiresolution Analysis of Signal Subspace Invariance Technique: a novel algorithm for blind source separation

Abstract: We developed (2001) a novel approach for blind source separation in multichannel signal processing environments. The technique, which relies on an inherent invariance property of the signal subspace across multiresolution levels obtained in the wavelet transform domain, showed robustness to amplitude and shift variations encountered in multi-unit neural recording environments. In this work, we extend the previous work to describe in detail a fast implementation of the algorithm and outline the criterion based on which the characterization of each source should be formulated. Results and performance evaluation that were not reported in the previous paper are illustrated in this paper.

Watermarking technique for authentication of 3-d polygonal meshes

Abstract: In this paper, we present a watermarking technique for authentication of 3-D polygonal meshes. The proposed technique is based on a wavelet-based multiresolution analysis to convert an original polygonal mesh model into a simplified mesh model and wavelet coefficient vectors. The embedding procedure is to modify the vertex of a simplified mesh model at a low resolution according to the order of norms of the wavelet coefficient vectors using a look-up table. The watermark extraction process is to restore the binary logo pattern by extracting a binary watermark bit from a look-up table corresponding to the watermarked simple mesh. The experimental results show that the proposed method is invariant to the location, scale and rotation transformation while detecting unauthorized modifications.

Gradient-driven update lifting for adaptive wavelets

Abstract: Over the past few years, wavelets have become extremely popular in signal and image processing applications. The classical linear wavelet transform, however, performs a homogeneous smoothing of the signal contents which, in some cases, is not desirable. This has led to a growing interest in (nonlinear) wavelet representations that can preserve discontinuities, such as transitions and edges. In this paper, we present the construction of adaptive wavelets by means of an extension of the lifting scheme. The basic idea is to choose the update filters according to some decision criterion which depends on the local characteristics of the input signal. We show that these adaptive schemes yield lower entropies than schemes with fixed update filters, a property that is highly relevant in the context of compression . Moreover, we analyze the effect of a scalar uniform quantization and the stability in such adaptive wavelet decompositions.